helixml / aispec

Visualization (diagram.png)

This outline describes the visualization for AI system architectures. The primary motivation is to provide a visual representation of complex systems, enhancing understanding and analysis.

Key Features:

  • Component Representation: Each node in the diagram represents a component of the AI system, such as data sources, models, or pipelines.
  • Relationship Visualization: Edges connecting nodes depict relationships between components, indicating data flow, dependencies, or interactions.
  • Customization: The visualization offers options for tailoring its appearance and content, enabling users to focus on specific aspects of the system.

Usage:

  1. Load System Data: The visualization utilizes data describing the AI system’s components and their relationships. This data can be provided in various formats, including configuration files, APIs, or databases.

  2. Generate Diagram: The tool processes the system data to generate a visual representation of the architecture.

  3. Customize Options: Users can adjust various parameters to customize the diagram, such as:

    • Component Styling: Different shapes, colors, and sizes can be used to represent different types of components.
    • Relationship Styling: The appearance of edges can be modified to distinguish different types of relationships.
    • Layout: The arrangement of nodes and edges can be customized to improve readability and highlight specific connections.
    • Filtering: Users can choose to display only specific components or relationships, enabling focused analysis.
    • Annotations: Text labels can be added to provide further information about components and their relationships.

Example:

The following snippet demonstrates how to visualize a simple AI system consisting of data preprocessing, a machine learning model, and an evaluation component. 

# Load system data
          system_data = {
              "components": [
                  {"name": "Data Preprocessing", "type": "data"},
                  {"name": "Machine Learning Model", "type": "model"},
                  {"name": "Evaluation", "type": "evaluation"}
              ],
              "relationships": [
                  {"source": "Data Preprocessing", "target": "Machine Learning Model", "type": "data flow"},
                  {"source": "Machine Learning Model", "target": "Evaluation", "type": "dependency"}
              ]
          }
          
          # Generate visualization
          visualization = Visualization(system_data)
          
          # Customize appearance
          visualization.set_component_style("model", shape="circle", color="blue")
          visualization.set_relationship_style("data flow", color="green")
          
          # Display the diagram
          visualization.show()

Source: https://github.com/helixml/aispec/